Use subprocess.run() to safely execute shell commands and capture output. It is recommended to pass parameters in lists to avoid injection risks; 2. When shell characteristics are required, you can set shell=True, but beware of command injection; 3. Use subprocess.Popen to realize real-time output processing; 4. Set check=True to throw exceptions when the command fails; 5. You can directly call chains to obtain output in a simple scenario; you should give priority to subprocess.run() in daily life to avoid using os.system() or deprecated modules. The above methods override the core usage of executing shell commands in Python.

It is recommended to use the in keyword to check whether a key exists in the dictionary, because it is concise, efficient and highly readable; 2. It is not recommended to use the get() method to determine whether the key exists, because it will be misjudged when the key exists but the value is None; 3. You can use the keys() method, but it is redundant, because in defaults to check the key; 4. When you need to get a value and the expected key usually exists, you can use try-except to catch the KeyError exception. The most recommended method is to use the in keyword, which is both safe and efficient, and is not affected by the value of None, which is suitable for most scenarios.

Use fmt.Scanf to read formatted input, suitable for simple structured data, but the string is cut off when encountering spaces; 2. It is recommended to use bufio.Scanner to read line by line, supports multi-line input, EOF detection and pipeline input, and can handle scanning errors; 3. Use io.ReadAll(os.Stdin) to read all inputs at once, suitable for processing large block data or file streams; 4. Real-time key response requires third-party libraries such as golang.org/x/term, and bufio is sufficient for conventional scenarios; practical suggestions: use fmt.Scan for interactive simple input, use bufio.Scanner for line input or pipeline, use io.ReadAll for large block data, and always handle

Use the requests library and set stream=True to achieve efficient file download and avoid memory overflow; 2. Read data in chunks through response.iter_content and write files in binary mode, which is suitable for all types of file types; 3. You can automatically extract the original file name from the URL and save it with urlparse and os.path.basename; 4. It is recommended to add timeout parameters and simulate browser request headers to improve download stability; 5. Make sure that the URL is a direct file link, and pay attention to the access restrictions and disk space of the target website. This method is suitable for most common scenarios, and the file has been successfully downloaded as: downloaded_file.pdf.

InstallJDK,setJAVA_HOME,installJavaExtensionPackinVSCode,createoropenaMaven/Gradleproject,ensureproperprojectstructure,andusebuilt-inrun/debugfeatures;1.InstallJDKandverifywithjava-versionandjavac-version,2.InstallMavenorGradleoptionally,3.SetJAVA_HO

SQLServer itself does not support serverless architecture, but the cloud platform provides a similar solution. 1. Azure's ServerlessSQL pool can directly query DataLake files and charge based on resource consumption; 2. AzureFunctions combined with CosmosDB or BlobStorage can realize lightweight SQL processing; 3. AWSathena supports standard SQL queries for S3 data, and charge based on scanned data; 4. GoogleBigQuery approaches the Serverless concept through FederatedQuery; 5. If you must use SQLServer function, you can choose AzureSQLDatabase's serverless service-free

Lazy loading only queries when accessing associations can easily lead to N 1 problems, which is suitable for scenarios where the associated data is not determined whether it is needed; 2. Emergency loading uses with() to load associated data in advance to avoid N 1 queries, which is suitable for batch processing scenarios; 3. Emergency loading should be used to optimize performance, and N 1 problems can be detected through tools such as LaravelDebugbar, and the $with attribute of the model is carefully used to avoid unnecessary performance overhead.

To remove duplicate elements in Python list, 1. Use set(): It is suitable for situations where elements are immutable and do not care about order. The syntax is list(set(original_list)), but it will disrupt the original order; 2. Use dict.fromkeys(): The recommended method is suitable for Python 3.7, which can maintain the order of the first occurrence of elements. The syntax is list(dict.fromkeys(original_list)), and the code is concise and efficient; 3. Use list deduction and seed collection: It is suitable for scenarios where custom deduplication logic is required. By traversing the list and recording the seen elements with the set, ensuring uniqueness; 4. Handling non-hashable types such as